Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

Max Tamlander et al. combine polygenic risk scores and clinical assessments to improve prediction of coronary artery disease and type 2 diabetes in European cohorts. Taken together, their results provide a useful method for preliminary cardiometabolic risk assessment in patients. Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8-4.6] up to 6.2 [4.6-7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.Peer reviewe

Genome-wide polygenic risk scores for colorectal cancer have implications for risk-based screening

Background: Hereditary factors, including single genetic variants and family history, can be used for targeting colorectal cancer (CRC) screening, but limited data exist on the impact of polygenic risk scores (PRS) on risk-based CRC screening. Methods: Using longitudinal health and genomics data on 453,733 Finnish individuals including 8801 CRC cases, we estimated the impact of a genome-wide CRC PRS on CRC screening initiation age through population-calibrated incidence estimation over the life course in men and women. Results: Compared to the cumulative incidence of CRC at age 60 in Finland (the current age for starting screening in Finland), a comparable cumulative incidence was reached 5 and 11 years earlier in persons with high PRS (80–99% and >99%, respectively), while those with a low PRS (< 20%) reached comparable incidence 7 years later. The PRS was associated with increased risk of post-colonoscopy CRC after negative colonoscopy (hazard ratio 1.76 per PRS SD, 95% CI 1.54–2.01). Moreover, the PRS predicted colorectal adenoma incidence and improved incident CRC risk prediction over non-genetic risk factors. Conclusions: Our findings demonstrate that a CRC PRS can be used for risk stratification of CRC, with further research needed to optimally integrate the PRS into risk-based screening.Peer reviewe

Missense variants in FRS3 affect body mass index in populations of diverse ancestries

Obesity is associated with adverse effects on health and quality of life. Improved understanding of its underlying pathophysiology is essential for developing counteractive measures. To search for sequence variants with large effects on BMI, we perform a multi-ancestry meta-analysis of 13 genome-wide association studies on BMI, including data derived from 1,534,555 individuals of European ancestry, 339,657 of Asian ancestry, and 130,968 of African ancestry. We identify an intergenic 262,760 base pair deletion at the MC4R locus that associates with 4.11 kg/m2 higher BMI per allele, likely through downregulation of MC4R. Moreover, a rare FRS3 missense variant, p.Glu115Lys, only found in individuals from Finland, associates with 1.09 kg/m2 lower BMI per allele. We also detect three other low-frequency FRS3 missense variants that associate with BMI with smaller effects and are enriched in different ancestries. We characterize FRS3 as a BMI-associated gene, encoding an adaptor protein known to act downstream of BDNF and TrkB, which regulate appetite, food intake, and energy expenditure through unknown signaling pathways. The work presented here contributes to the biological foundation of obesity by providing a convincing downstream component of the BDNF-TrkB pathway, which could potentially be targeted for obesity treatment.</p

Missense variants in FRS3 affect body mass index in populations of diverse ancestries

Obesity is associated with adverse effects on health and quality of life. Improved understanding of its underlying pathophysiology is essential for developing counteractive measures. To search for sequence variants with large effects on BMI, we perform a multi-ancestry meta-analysis of 13 genome-wide association studies on BMI, including data derived from 1,534,555 individuals of European ancestry, 339,657 of Asian ancestry, and 130,968 of African ancestry. We identify an intergenic 262,760 base pair deletion at the MC4R locus that associates with 4.11 kg/m2 higher BMI per allele, likely through downregulation of MC4R. Moreover, a rare FRS3 missense variant, p.Glu115Lys, only found in individuals from Finland, associates with 1.09 kg/m2 lower BMI per allele. We also detect three other low-frequency FRS3 missense variants that associate with BMI with smaller effects and are enriched in different ancestries. We characterize FRS3 as a BMI-associated gene, encoding an adaptor protein known to act downstream of BDNF and TrkB, which regulate appetite, food intake, and energy expenditure through unknown signaling pathways. The work presented here contributes to the biological foundation of obesity by providing a convincing downstream component of the BDNF-TrkB pathway, which could potentially be targeted for obesity treatment.</p

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

Laajamittaiset biopankkihankkeet ja kaupalliset tietokannat varastoivat miljoonilta yksilöiltä kerättyä genomitietoa, ja nopeasti lisääntyvien terveys- ja genomitietokantojen myötä on tarve kehittää genomitietoa hyödyntäviä työkaluja sairauksien ennaltaehkäisyyn. Tässä tutkimuksessa esitetään genomipohjaisten riskityökalujen luominen ja validointi kahdelle yleiselle kardiometaboliselle sairaudelle, sepelvaltimotaudille ja tyypin 2 diabetekselle. Analyyseissämme käytettäviin aineistoihin sisältyvät FinnGen-tutkimus (N = 309,154) ja UK Biobank -tutkimus (N = 343,672). Riskityökalut yhdistävät genominlaajuisen polygeenisen riskipisteytyksen yksinkertaisiin kyselypohjaisiin riskitekijöihin, mukaan lukien demografisiin, elämäntapa-, lääkitys-, ja liitännäissairaustietoihin, mahdollistaen riskilaskennan genomitietoa sisältävissä tietokannoissa. Verrattuna yleisesti käytettyihin sepelvaltimotaudin ja tyypin 2 diabeteksen kliinisiin riskilaskureihin, riskityökaluilla on vähintään vastaava erottelukyky, parantunut uudelleenluokittelu (net reclassification improvement 3.7 [95% CI 2.8–4.6] ja 6.2 [4.6–7.8] välillä), ja riskityökalujen ennustekykyä voidaan lisäksi parantaa tavallisilla rasva-arvojen ja verenpaineen mittauksilla. Riskityökalut tarjoavat tehokkaan ja yksinkertaisen menetelmän alustavaan kardiometaboliseen riskiarvioon henkilöille, joilla on genomitietoa käytettävissä. Tämä mahdollistaa riskitason ensiarvion ilman verikokeita tai terveydenhuollon ammattilaisen syöttämää tietoa.Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available

Large-Scale Functional Characterization of Low-Density Lipoprotein Receptor Gene Variants Improves Risk Assessment in Cardiovascular Disease

Limited access to functional information of genetic variants reduces the applicability of genetic tools for precision medicine applications in cardiovascular disease. We established an automated analysis platform based on multiplexed high-content imaging and derived in-depth functional data for several hundred LDLR gene variants. Residual low-density lipoprotein receptor activity of genetic variants impacted the risk for cardiovascular disease and elevated low-density lipoprotein cholesterol as well as the utilization of lipid-lowering and combination therapy. This enables increased risk stratification for carriers of LDLR gene variants and opens up new opportunities for improved diagnosis, risk assessment, and treatment selection in familial hypercholesterolemia. (JACC Basic Transl Sci. 2025;10:170-183) (c) 2025 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Linking biological variation to outcomes of statin treatment in the general population

Background and aims Large interindividual variability in achieved low-density lipoprotein cholesterol (LDL-C) concentration is known for statin recipients. Systematic profiling of cellular lipid trafficking pathways was performed to elucidate how biological variation contributes to treatment outcomes of statin therapy. Methods Using a multiplexed imaging platform 26 readouts for cellular lipid trafficking were obtained from leukocyte subpopulations for each person, including LDL uptake (UPT), lipid storage (LiM) and three combined lipid trafficking scores (LT-P, LT-R, LT). With this pipeline 400 subjects of the FINRISK 2012 Study were analysed, including 200 recipients of cholesterol-lowering medication. Results Large interindividual variability of cellular LDL uptake and lipid storage was observed. Both LDL uptake and LT score associated negatively with LDL-C for statin recipients and the associations strengthened with increase in statin intensity, explaining up to 25% of LDL-C variability. Integration of LT score with a polygenic risk score for LDL-C further increased the association strength. High-intensity statin (HIS) recipients in the lowest quintile of LT score displayed a pro-atherogenic lipoprotein profile containing more VLDL, IDL and LDL particles with altered lipid content as compared to subjects in the highest quintile of the score. Subjects in the lowest quintile of the LT-R score had lower odds to be at their target LDL-C level and subjects in the lowest quintile of LiM score had higher odds to experience a cardiovascular event as compared to the rest of the HIS recipients. Conclusions Interindividual variation in cellular lipid trafficking pathways can contribute to the variability of statin therapy outcomes and provide new opportunities for treatment optimization and risk assessment in cardiovascular disease.</jats:p

High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases

Peer reviewe

Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women

PURPOSE Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure. METHODS In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes. RESULTS Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers. CONCLUSION Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.Peer reviewe

Inflammatory and infectious upper respiratory diseases associate with 41 genomic loci and type 2 inflammation

AbstractInflammatory and infectious upper respiratory diseases (ICD-10: J30-J39), such as diseases of the sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyze genome-wide association to eight upper respiratory diseases (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. Aiming to understand which genetic loci contribute to susceptibility to upper respiratory diseases in general and its subtypes, we detect 41 independent genome-wide significant loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in nine genes, including three linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci, and inflammatory bowel diseases and other immune-mediated disorders at pharyngeal disease loci. Upper respiratory diseases also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among upper respiratory diseases that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.</jats:p