Polygenic risk scores (PRS) have shown promise in predicting susceptibility to common diseases. 1-3 We estimated their added value in clinical risk prediction of five common diseases, using largescale biobank data (FinnGen; N=135,300), and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease (CHD), type 2 diabetes (T2D), atrial fibrillation (AF), and breast and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to average PRS, having a high PRS contributed to 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRS improved model discrimination over age and sex in T2D, AF, breast cancer, and prostate cancer, and over clinical risk in T2D, breast cancer, and prostate cancer. In all diseases, PRS improved reclassification over clinical thresholds, with largest net reclassification improvements for early-onset CHD, AF, and prostate cancer. This study provides evidence for the additional value of PRS in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies.
Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, 56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2–2.4) risk of hypertension and 10.6 years (95% CI, 9.9–11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5–1.7) for late-onset hypertension (age ≥55 years) but 2.8-fold (95% CI, 2.6–2.9) for early-onset hypertension (age <55 years). Elevated systolic BP PRS also conferred 1.3-fold (95% CI, 1.2–1.4) risk of CVD and 2.3 years (95% CI, 1.6–3.1) earlier onset. In FINRISK, systolic and diastolic BP PRSs improved clinical risk prediction of hypertension (but not CVD), increasing the C statistics by 0.7% (95% CI, 0.3–1.1). We demonstrate that genetic information improves hypertension risk prediction. BP PRSs together with traditional risk factors could improve prediction of hypertension and particularly early-onset hypertension, which confers substantial CVD risk.
Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
Background Several small‐scale animal studies have suggested that gut microbiota and blood pressure (BP) are linked. However, results from human studies remain scarce and conflicting. We wanted to elucidate the multivariable‐adjusted association between gut metagenome and BP in a large, representative, well‐phenotyped population sample. We performed a focused analysis to examine the previously reported inverse associations between sodium intake and Lactobacillus abundance and between Lactobacillus abundance and BP. Methods and Results We studied a population sample of 6953 Finns aged 25 to 74 years (mean age, 49.2±12.9 years; 54.9% women). The participants underwent a health examination, which included BP measurement, stool collection, and 24‐hour urine sampling (N=829). Gut microbiota was analyzed using shallow shotgun metagenome sequencing. In age‐ and sex‐adjusted models, the α (within‐sample) and β (between‐sample) diversities of taxonomic composition were strongly related to BP indexes ( P <0.001 for most). In multivariable‐adjusted models, β diversity was only associated with diastolic BP ( P =0.032). However, we observed significant, mainly positive, associations between BP indexes and 45 microbial genera ( P <0.05), of which 27 belong to the phylum Firmicutes . Interestingly, we found mostly negative associations between 19 distinct Lactobacillus species and BP indexes ( P <0.05). Of these, greater abundance of the known probiotic Lactobacillus paracasei was associated with lower mean arterial pressure and lower dietary sodium intake ( P <0.001 for both). Conclusions Although the associations between overall gut taxonomic composition and BP are weak, individuals with hypertension demonstrate changes in several genera. We demonstrate strong negative associations of certain Lactobacillus species with sodium intake and BP, highlighting the need for experimental studies.
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.
Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson’s disease3,4, suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns5–8, the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9, 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work10, confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.
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