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

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

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

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

Inflammatory 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.The shared genetics between upper respiratory diseases have not been well studied. Here, the authors find shared and distinct genetic loci for pharyngeal and sinonasal inflammatory conditions, which show shared heritability with autoimmune conditions and immune deficiency, highlighting the TNFR2 pathway.Peer reviewe

Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5

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