Distinct genetic liability profiles define clinically relevant patient strata across common diseases.

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Genome-wide association and polygenic risk score estimation of type 2 diabetes mellitus in Kinh Vietnamese-A pilot study.

A genome-wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic-associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10-6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62-0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia.

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression.

BACKGROUND: Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual "omics" signature that distinguishes subjects with varying cognitive profiles. RESULTS: We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging. CONCLUSIONS: These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

Using polygenic scores in combination with symptom rating scales to identify attention-deficit/hyperactivity disorder.

BACKGROUND: The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. METHODS: Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. RESULTS: The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2ASRS = 61.11%, R2ASRS + PGS=61.69%), the WURS by 0.61pp (R2WURS = 77.33%, R2WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2ASRS + WURS=80.84%, R2ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2family = 28.06%, R2family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. CONCLUSION: We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined.

A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry.

The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571-12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

Association of Occupational Dysfunction and Hospital Admissions With Different Polygenic Profiles in Bipolar Disorder.

OBJECTIVE: Many but not all persons with bipolar disorder require hospital care because of severe mood episodes. Likewise, some but not all patients experience long-term occupational dysfunction that extends beyond acute mood episodes. It is not known whether these dissimilar outcomes of bipolar disorder are driven by different polygenic profiles. Here, polygenic scores (PGSs) for major psychiatric disorders and educational attainment were assessed for associations with occupational functioning and psychiatric hospital admissions in bipolar disorder. METHODS: A total of 4,782 patients with bipolar disorder and 2,963 control subjects were genotyped and linked to Swedish national registers. Longitudinal measures from at least 10 years of registry data were used to derive percentage of years without employment, percentage of years with long-term sick leave, and mean number of psychiatric hospital admissions per year. Ordinal regression was used to test associations between outcomes and PGSs for bipolar disorder, schizophrenia, major depressive disorder, attention deficit hyperactivity disorder (ADHD), and educational attainment. Replication analyses of hospital admissions were conducted with data from the Bipolar Disorder Research Network cohort (N=4,219). RESULTS: Long-term sick leave and unemployment in bipolar disorder were significantly associated with PGSs for schizophrenia, ADHD, major depressive disorder, and educational attainment, but not with the PGS for bipolar disorder. By contrast, the number of hospital admissions per year was associated with higher PGSs for bipolar disorder and schizophrenia, but not with the other PGSs. CONCLUSIONS: Bipolar disorder severity (indexed by hospital admissions) was associated with a different polygenic profile than long-term occupational dysfunction. These findings have clinical implications, suggesting that mitigating occupational dysfunction requires interventions other than those deployed to prevent mood episodes.

Pathways between a polygenic index for education and years of completed schooling: the presentation of self and assessment of others.

Polygenic scores (PGS) are broadly misconstrued as reflecting direct causal genetic effects on their respective phenotypes. While this assumption might be accurate for some anthropometric traits like height, more complex traits such as educational attainment show very large indirect effects that stem from many sources. One unexplored source of confounding is the possibility of evocative gene-environment correlation (rGE). Using data from the National Longitudinal Study of Adolescent to Adult Health, we examine the relationship between interviewer assessments of respondent appearance as a function of education PGS. We show a bivariate association between educational PGS and 1) perceived grooming, 2) physical attractiveness, and 3) personality. We then regress years of education on the educational PGS and show that very little of the association (~1-2%) is mediated by attractiveness or personality but 7.5% of the baseline association is confounded with how others may perceive grooming. These results highlight the importance of social-behavioral mechanisms that may link specific genotypes to successful transitions through high school and college and continue to bridge research from the social and biological sciences.

Genetic drivers and cellular selection of female mosaic X chromosome loss.

Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.

Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors.

PURPOSE: We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. METHODS: This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. RESULTS: Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. CONCLUSION: CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.

Intrinsic Capacity, Polygenic Risk Score, APOE Genotype, and Risk of Dementia: A Prospective Cohort Study Based on the UK Biobank.

BACKGROUND AND OBJECTIVES: The World Health Organization recently released a novel metric for healthy aging: intrinsic capacity (IC). The relationship between IC and the incidence of dementia, and its subtypes, is unknown. We aimed to analyze the relationship between IC and the incidence of dementia and its subtypes. Moreover, we tested whether genetic susceptibility to dementia could be modified by IC. METHODS: This cohort study involved 366,406 participants from the UK Biobank between 2006 and 2010. We analyzed 7 factors that reflected functional status across 4 IC domains to compute a comprehensive IC deficit score. Cox models were used to elucidate the relationship between the IC deficit score and the incidence of dementia. RESULTS: Among the 366,406 participants, 5,207 cases of dementia were documented, encompassing 2,186 and 1,175 cases of Alzheimer disease (AD) and vascular dementia (VD), respectively. Compared with participants with an IC score of 0, individuals with an IC score of 4+ had a markedly elevated risk of dementia (hazard ratio [HR] 2.17, 95% CI 1.92-2.45). In the joint analysis, for participants with a high polygenic risk score (PRS) and an IC score of 4 or more, the HR of all-cause dementia was 8.11 (95% CI 6.28-10.47) compared with individuals with a low PRS and an IC score of 0. Similar results were seen in the AD and VD groups. DISCUSSION: In summary, IC is associated with a higher risk of dementia, particularly in those combined with genetically predisposed to dementia.

MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease.

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.

A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation.

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17-1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS.

Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color.

Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.

Family-based GWAS for dental class I malocclusion and clefts.

BACKGROUND: Individuals born with cleft lip and/or palate who receive corrective surgery regularly have abnormal growth in the midface region such that they exhibit premaxillary hypoplasia. However, there are also genetic contributions to craniofacial morphology in the midface region, so although these individuals appear to have Class III skeletal discrepancy, their molar relationship may be Class I. Past genome-wide association studies (GWASs) on skeletal Class II and III malocclusion suggested that multiple genetic markers contribute to these phenotypes via a multifactorial inheritance model, but research has yet to examine the genetic markers associated with dental Class I malocclusion. Thus, our goal was to conduct a family based GWAS to identify genes across the genome that are associated with Class I malocclusion, as defined by molar relations, in humans with and without clefts. METHODS: Our cohort consisted of 739 individuals from 47 Filipino families originally recruited in 2006 to investigate the genetic basis of orofacial clefts. All individuals supplied blood samples for DNA extraction and genotyping, and a 5,766 single nucleotide polymorphism (SNP) custom panel was used for the analyses. We performed a transmission disequilibrium test for participants with and without clefts to identify genetic contributors potentially involved with Class I malocclusion. RESULTS: In the total cohort, 13 SNPs had associations that reached the genomic control threshold (p < 0.005), while five SNPs were associated with Class I in the cohort of participants without clefts, including four associations that were identified in the total cohort. The associations for the SNPs ABCA4 rs952499, SOX1-OT rs726455, and RORA rs877228 are of particular interest, as past research found associations between these genes and various craniofacial phenotypes, including cleft lip and/or palate. CONCLUSIONS: These findings support the multifactorial inheritance model for dental Class I malocclusion and suggest a common genetic basis for different aspects of craniofacial development.

Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample.

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Genotype × environment interactions in gene regulation and complex traits.

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses.

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.